Journal of Financial Economics - Gies College of Business · 2020-01-10 · 478 J.R. Brown et al. /...
Transcript of Journal of Financial Economics - Gies College of Business · 2020-01-10 · 478 J.R. Brown et al. /...
Journal of Financial Economics 121 (2016) 477–495
Contents lists available at ScienceDirect
Journal of Financial Economics
journal homepage: www.elsevier.com/locate/finec
Decision-making approaches and the propensity to default:
Evidence and implications
Jeffrey R. Brown
a , Anne M. Farrell b , Scott J. Weisbenner c , ∗
a Department of Finance, University of Illinois at Urbana-Champaign and NBER, 260E Wohlers Hall, 1206 S. Sixth St., Champaign, IL
61820, United States b Department of Accountancy, Farmer School of Business, Miami University, 800 East High Street, Oxford, OH 45056, United States c Department of Finance, University of Illinois at Urbana-Champaign and NBER, 340 Wohlers Hall, 1206 S. Sixth St., Champaign, IL 61820,
United States
a r t i c l e i n f o
Article history:
Received 5 February 2015
Revised 5 October 2015
Accepted 6 November 2015
Available online 30 May 2016
JEL classifications:
D03
D14
G11
Keywords:
Default options
Retirement
Pensions
Behavioral economics
Decision making
a b s t r a c t
This paper examines heterogeneity in the responsiveness to default options in a large state
retirement plan, focusing on individuals’ decision-making approaches as well as their eco-
nomic and demographic characteristics. Analyses of a survey of plan participants show
that procrastination and the need for cognitive closure are important determinants of the
likelihood of default. This paper also explores an important implication of defaulting—
individuals who default are significantly more likely to subsequently express a desire to
enroll in a different plan. The desire to change plans is also correlated with numerous
economic and decision-making characteristics, including procrastination.
© 2016 Elsevier B.V. All rights reserved.
1. Introduction
One of the most influential contributions of behavioral
economics to business practice and public policy over the
past decade has been to demonstrate the substantial power
of default options in influencing human behavior. Nowhere
is this influence more apparent than in the area of re-
tirement plan design and policy. Compelling evidence that
changing the default option dramatically increases partici-
pation and savings in 401(k) plans (e.g., Madrian and Shea,
2001; Choi, Laibson, Madrian, and Metrick, 2002, 2004 )
∗ Corresponding author. Tel.: + 1 217 333 0872; fax: + 1 217 244 3102.
E-mail addresses: [email protected] (J.R. Brown),
[email protected] (A.M. Farrell), [email protected] (S.J.
Weisbenner).
http://dx.doi.org/10.1016/j.jfineco.2016.05.010
0304-405X/© 2016 Elsevier B.V. All rights reserved.
prompted the U.S. government in the 2006 Pension Pro-
tection Act to provide safe harbor provisions for firms
offering automatic enrollment in defined contribution re-
tirement plans. In recent years, there has been a dramatic
increase in the use of automatic enrollment, automatic es-
calation of contributions, and automatic portfolio alloca-
tion and rebalancing both in the U.S. and abroad. For ex-
ample, the Plan Sponsor Council of America’s (2012) 55th
Annual Survey finds that 46% of surveyed defined contribu-
tion plans have an automatic enrollment feature in 2011,
while Munnell and Sundén (2004) report that 7% of plan
sponsors offered automatic enrollment in 1999. There have
also been calls to extend the logic of defaults to the post-
retirement payout phase of retirement plans by encourag-
ing automatic annuitization ( Gale, Iwry, John, and Walker,
2008 ).
478 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Although countless studies show profound effects of
defaults on behavior, there is still limited understand-
ing of why defaults have such large effects overall, and,
equally importantly, why there is heterogeneity in the re-
sponsiveness to defaults. This paper provides an empiri-
cal analysis of the determinants of a default decision in a
large public plan that offers an irrevocable choice among
three retirement plans: a traditional defined benefit plan,
a portable defined benefit plan, and a defined contribu-
tion plan. In addition to examining the full range of eco-
nomic and demographic factors, analyses also shed light on
the role of several relevant individual decision-making ap-
proaches identified in the judgment and decision-making
literature. These include approaches in the presence of
decision conflict, or uncertainty about which course of
action to take ( Mann, Burnett, Radford, and Ford, 1997 );
indecision ( Frost and Shows, 1993 ); the propensity to re-
gret ( Schwartz, Ward, Monterosso, Lyubomirsky, White,
and Lehman, 2002 ), and the need for cognitive closure,
or the desire to come to an answer ( Roets and Van Hiel,
2011 ). Measures of economic, demographic, and decision-
approach factors that affect defaults are captured using a
broad survey conducted among participants in the State
Universities Retirement System (SURS) of Illinois. In all,
over 60 0 0 public university employees in the State of Illi-
nois responded to the survey during the fall of 2012.
We first study whether individuals made an active re-
tirement plan choice or defaulted into the traditional de-
fined benefit plan (individuals are defaulted six months
after joining the system unless they make an active elec-
tion prior to that date). Approximately 27% of survey re-
spondents defaulted whereas the remainder actively chose
among the three plans. Numerous demographic and eco-
nomic variables influence the propensity to default. For
example, higher income and higher net worth individu-
als are significantly less likely to default, as are women,
those with higher self-assessed investment skills, those
with greater knowledge of the retirement system, and a
higher education level.
With regard to decision-making approaches, results
show that a tendency toward procrastination is signifi-
cantly positively correlated with the likelihood of default.
Numerous authors have speculated that procrastination is
a plausible reason for default, although this has not been
shown empirically. 1 This finding is quite intuitive: those
with a tendency to procrastinate are less likely to make an
active decision before the default deadline. It is also con-
sistent with a body of economic theory that portrays pro-
crastination as an outcome of present-biased preferences
( Akerlof, 1991; O’Donoghue and Rabin, 1999 ). In this view,
people with present-biased preferences tend to systemat-
ically overweight the cost of making a decision today, a
tendency that manifests itself as procrastination.
Results also show that individuals with a strong need
for cognitive closure are less likely to default. Kruglanski
(1990 , p. 337) defines need for closure as a desire for “an
1 Brown and Previtero (2015) do not study default behavior, but do
present evidence consistent with procrastinators being more likely to
stick with the default portfolio allocation.
answer on a given topic, any answer...compared to confu-
sion and ambiguity.” A need for closure is therefore a nat-
ural factor to explore that can potentially mitigate default
behavior.
Having established that individual decision-making ap-
proaches are correlated with the likelihood of default, we
then turn to understanding how individuals evaluate the
suitability of their retirement plan ex post. In addition to
providing some insight into the individual welfare impli-
cations, this analysis is also useful for ruling out the possi-
bility that procrastinators might accept the default because
they believe it is the best option. For example, self-aware
procrastinators might decide that the Traditional Plan is
best for them because it requires less active oversight or
because it removes the temptation to cash out the plan
upon retirement. Survey respondents were asked, “If you
could go back in time and re-do your original pension
choice (assuming the rules when you joined SURS are still
in place), which plan would you choose?”, and also rated
the strength of their desire to choose a different plan. Re-
sults show that respondents who defaulted into the Tradi-
tional Plan are 21% less likely to want to select the same
plan if given a chance to re-do their choice. This result is
true even relative to those who actively chose the same
plan into which others were defaulted, suggesting that it is
the default behavior rather than the plan itself that is driv-
ing the desire to switch to a different plan. In addition, the
proportion of those who would “strongly desire” to switch
plans is significantly greater among defaulters than among
active choosers.
We relate the desire to change plans to the same set
of economic, demographic, and decision-making character-
istics studied above. Results again show that procrastina-
tion is important: individuals who procrastinated their way
into the default are significantly more likely to desire to
be in a different plan, which argues against the alternative
hypothesis that procrastinators default into the Traditional
Plan because they believe it is the best option for them.
Respondents who are buck-passers—those that are content
to leave decisions to others—are significantly less likely to
express a desire to switch plans.
These results are relevant for public policy, given the
broad use of default options in public and private retire-
ment plans. In particular, the findings that procrastination
leads to defaults and that procrastinators are more likely to
subsequently express a desire to be in a different plan are
important for assessing the welfare consequences of de-
faults. The use of defaults is often portrayed as a Pareto
improvement because a well-designed default can guide
individuals into making potentially welfare-improving de-
cisions while still providing the freedom to choose. But if
individuals end up dissatisfied with the results of the de-
fault, especially in settings like ours in that the default
is irreversible, then defaults may not be Pareto improv-
ing. Other authors (e.g., Carroll, Choi, Laibson, Madrian, and
Metrick, 2009; Carlin, Gervais, and Manso, 2013 ) note that
various characteristics of the choice setting will help de-
termine whether automatic enrollment defaults are prefer-
able to or inferior to other options, such as forced choice
or voluntary choice (i.e., when enrollment requires an ac-
tive choice).
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 479
2 Glaeser (2006) also discusses a number of other criticisms and neg-
ative consequences of over-reliance on libertarian paternalism as a guide
to policy, including: (i) soft paternalism can pave the way towards stricter
forms of paternalism that reduce welfare by reducing individual choice;
(ii) soft paternalism may rely on stigmatizing behaviors, which can then
lead to negative consequences for those who choose to engage in those
behaviors; (iii) relative to governments and organizations that design pa-
ternalistic policies, individuals face stronger incentives to make choices
that improve their own welfare; and, (iv) paternalism often relies on per-
suasion and governments and organizations have an incentive to abuse
persuasion-based systems to enhance their own power.
For example, research shows that poorly designed de-
faults can reduce welfare if employees fail to later adjust
away from the defaults to suit their needs ( Choi, Laibson,
Madrian, and Metrick, 20 02, 20 04, 20 06; Beshears, Choi,
Laibson, and Madrian, 2008, 2010 ) and that optimal de-
faults can vary depending upon participant characteristics
( Carroll, Choi, Laibson, Madrian, and Metrick, 2009; Car-
lin, Gervais, and Manso, 2013; Goda and Manchester, 2013 ).
Our results add to this list and suggest that reliance on
defaults can lead to dissatisfaction with the outcome. Be-
cause our setting is one with particularly high stakes and
is irreversible, the magnitude of our findings should not
be generalized to all settings in which defaults might be
used. Nonetheless, our results suggest caution against over-
relying on defaults when there is a high-stakes, irreversible
choice in a population for which there is heterogeneity in
the optimal choice.
This paper proceeds as follows. Section 2 summa-
rizes prior literature on defaults, and Section 3 briefly
describes prior literature related to individual judgment
and decision-making approaches in complex settings.
Section 4 provides background on the SURS retirement sys-
tem, and Section 5 includes details of our survey design.
Section 6 presents results of analyses of factors associated
with the likelihood of default and with the desire to subse-
quently make a different choice. Section 7 includes a sum-
mary and conclusion.
2. Prior literature on defaults
The earliest work on defaults in the retirement space
showed that changing the 401(k) enrollment procedure to
one in which a participant must actively opt out of a plan
rather than actively opt in dramatically increases plan par-
ticipation ( Madrian and Shea, 2001 ). Additional research
shows that changing the default savings rate and default
investment allocations exerts a powerful influence on sav-
ings plan outcomes ( Choi, Laibson, Madrian, and Metrick,
20 02, 20 04 ). This early work, as well as industry experi-
ence, propelled policy conversations that led to the U.S.
government paving the way for more widespread use of
automatic enrollment in defined contribution retirement
plans via the 2006 Pension Protection Act (PPA). The PPA
and subsequent regulatory actions have also encouraged
the widespread use of “Qualified Default Investment Alter-
natives” (QDIAs) as default portfolio allocations, as well as
the use of automatic escalation of contributions. Many fi-
nancial services firms also now offer automatic rebalancing
of portfolios. There have also been policy proposals to en-
act automatic annuitization as a default distribution strat-
egy after retirement ( Gale, Iwry, John, and Walker, 2008;
Brown, 2009 ).
The idea that governments and organizations can influ-
ence behavior through the use of defaults and other forms
of non-binding approaches is often referred to in academic
and popular literature as “soft paternalism” or “libertarian
paternalism” ( Sunstein and Thaler, 2003; Thaler and Sun-
stein, 2003, 2008 ). Some proponents of libertarian pater-
nalism suggest that careful design of policies and defaults
can do more to increase welfare than can providing in-
formation to increase individuals’ knowledge about their
choices ( Sunstein and Thaler, 2003; Benartzi and Thaler,
2007 ). While retirement plan design has been a very vis-
ible and important application of this concept, the effect
of defaults on individual choice is recognized in other do-
mains as well, including email marketing ( Johnson, Bell-
man, and Lohse, 2002 ), health care ( Halpern, Ubel, and
Asch, 2007 ), health club memberships ( DellaVigna and
Malmendier, 2006 ), insurance ( Johnson, Hershey, Meszaros,
and Kunreuther, 1993 ), and organ donation ( Johnson and
Goldstein, 2003; Abadie and Gay, 2006 ).
While libertarian paternalism is often portrayed as
an ideological “win-win” by guiding behavior while pre-
serving individual choice, literature has begun to raise
potentially negative consequences. For example, Glaeser
(2006) points out that there is a danger of leading indi-
viduals to suboptimal outcomes because those who design
policies and choose default options likely bring their own
incentives and biases to that task. 2 Some studies show
that poorly designed defaults, such as those with low de-
fault savings rates or excessively conservative asset alloca-
tions, can reduce welfare if employees fail to later adjust
away from the defaults to suit their needs ( Choi, Laibson,
Madrian, and Metrick, 20 02, 20 04, 20 06; Beshears, Choi,
Laibson, and Madrian, 2008 ). At another extreme, Beshears,
Choi, Laibson, and Madrian (2010) examine a setting in
which the default savings rate for a defined contribution
retirement plan is extremely high, and find that the rate is
suboptimal for all employees.
Other research has explored conditions under which
defaults are more or less likely to improve social welfare.
Carroll, Choi, Laibson, Madrian, and Metrick (2009) con-
trast forced active choice, automatic enrollment defaults,
and non-automatic enrollment defaults in savings plans
and find that forced choice is optimal when partici-
pants could procrastinate or have heterogeneous prefer-
ences, while automatic enrollment is optimal when par-
ticipants are financially illiterate. Similarly, Carlin, Gervais,
and Manso (2013) model conditions under which provid-
ing default options for financial decisions could be op-
timal; they find that even well-thought-out defaults can
be detrimental to welfare when participants have hetero-
geneous attributes (and less is known about them) and
when the economic stakes of the decision are large. Goda
and Manchester (2013) examine the welfare effects of age-
based defaults and find that varying the default option by
age groups can result in welfare gains relative to a single
default for all age groups.
Although prior literature provides insights into when
defaults may or may not be optimal, the empirical evi-
dence regarding who defaults and why is more limited.
480 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Beshears, Choi, Laibson, and Madrian (2008) propose three
reasons that individuals may default, including the com-
plexity of the decision, the belief that the default is a sig-
nal or endorsement of the best choice, or that procrasti-
nators never get around to making a decision. Understand-
ing why people default is crucial for evaluating the welfare
consequences of relying on defaults as opposed to other
interventions.
This paper begins to address the empirical gap by ex-
ploring economic, demographic, and individual decision-
making determinants of default behavior in a high-stakes
setting. Although our setting does not allow us to examine
the welfare effects of default, it does allow us to shed light
on this question by examining individuals’ post-decision
subjective satisfaction with the plan in which they are en-
rolled. This subjective satisfaction is also important for em-
ployers who design defaults. After all, employers have an
incentive to ensure that the significant sums they spend
to provide retirement benefits are valued by employees
at least as much as a comparable sum spent on wages
( Gustman, Mitchell, and Steinmeier, 1994; Gustman and
Steinmeier, 2005 ).
3. Prior research on decision-making approaches
Standard economic models of rational consumers as-
sume that individuals make decisions by maximizing ex-
pected utility. Indeed, even with the insights of behavioral
economics, most economic models of decision-making are
still based upon an assumption of optimization, albeit oc-
casionally with non-standard preferences (e.g., loss aver-
sion or hyperbolic discounting). One of the reasons that
economists have found default behavior interesting is that
it is difficult to reconcile the powerful effect of default op-
tions with standard economic models.
There is growing acceptance in economics that not all
individuals approach decision-making in the same man-
ner, and thus their decisions themselves can diverge from
an economics-based definition of optimality. For exam-
ple, Choi, Kariv, Müller, and Silverman (2014) conducted
an experiment to test the quality of decisions as mea-
sured by their consistency with Generalized Axioms of Re-
vealed Preference (GARP) and find evidence of substan-
tial heterogeneity in decision quality. Lusardi and Mitchell
(2014) summarize a large literature that shows substantial
heterogeneity in financial literacy and its implications for
retirement well-being (among other outcomes).
A long history of research shows that, when faced with
complex decisions, individuals frequently adopt simplifying
decision strategies ( Wood, 1986; Campbell, 1988; Payne,
Bettman, and Johnson, 1993; Sethi-Iyengar, Huberman, and
Jiang, 2004; Benartzi and Thaler, 2007; Bonner, 2008 ). For
example, an individual could only consider a subset of in-
formation, and the information chosen may not necessar-
ily reflect the relevance of the information to the choice.
Individuals could also speed up information processing in
response to time pressure, which can introduce error into
the choice process. Alternately, they could adopt a simpler
processing strategy, which at the extreme can be avoiding
choice all together by accepting a default ( Payne, Bettman,
and Johnson, 1993; Sethi-Iyengar, Huberman, and Jiang,
2004; Benartzi and Thaler, 2007; Beshears, Choi, Laibson,
and Madrian, 2008 ).
In drawing on this rich judgment and decision-making
literature, our research focuses on what has been called
“decision-coping,” of which our discussion below draws di-
rectly from Mann, Burnett, Radford, and Ford (1997) . Par-
ticularly relevant is prior literature on decision conflict,
which assumes that “stress engendered by decisional con-
flict is a major determinant of failure to achieve high qual-
ity decision making” ( Mann, Burnett, Radford, and Ford,
1997 , p. 2). In brief, when faced with a difficult decision,
not all individuals respond in the same way. A subset will
respond in a manner that would be consistent with eco-
nomic models of optimizing behavior, i.e., by collecting and
analyzing relevant information and choosing the outcome
that maximizes utility. However, other individuals will ig-
nore information and continue the present course of ac-
tion; some will adopt whichever course is most strongly
recommended; some will procrastinate or shift responsibil-
ity for the choice; and some will “impulsively seize upon
hastily contrived solutions that seem to promise immedi-
ate relief” ( Mann, Burnett, Radford, and Ford, 1997 , p. 2).
As described more fully in Section 5 , the survey in-
cludes questions that capture decision approaches identi-
fied in prior literature that are likely to manifest in our
decision context—a complex, high-stakes, and irrevocable
financial choice. Because procrastination is hypothesized
to be a possible cause of defaults in the prior litera-
ture ( Beshears, Choi, Laibson, and Madrian, 2008 ), a pri-
ori this is an important characteristic to measure. The sur-
vey includes the procrastination scale from the Melbourne
Decision-Making Questionnaire, which is extensively used
and validated in prior literature. This questionnaire is de-
signed to capture approaches individuals can adopt when
facing complex and often high-stakes decisions within a
limited time period, which is characteristic of our plan-
choice setting. Along with procrastination, the Melbourne
Questionnaire measures three other approaches to cop-
ing with decision conflict: vigilance, hypervigilance, and
buck-passing. As such, survey measures capture the vari-
ous decision-making approaches a respondent uses rather
than just procrastination alone.
A review of prior literature on decision-making sug-
gests three other general decision-making tendencies that
are highly relevant to the default setting (and our analysis
of whether defaulters subsequently wish to switch plans).
First, a tendency to regret decisions could be particularly
important for a high-stakes, irrevocable decision such as
that studied in our paper. This measure also allows us to
disentangle whether a desire to switch plans stems from
a general tendency to regret rather than a procrastination
approach to decisions. Second, a measure of indecisiveness
provides a means to disentangle whether defaulting stems
from a general tendency to waiver about decisions or a
tendency to put off making them in the first place. Third, a
measure of the need for cognitive closure, defined as “a de-
sire for an answer on a given topic, any answer...compared
to confusion and ambiguity” ( Kruglanski, 1990 , p. 337),
provides a means to disentangle whether those who ac-
tively choose plans did so because they relied on more
systematic information processing (as captured by, for
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 481
example, a vigilance approach) rather than the desire to
simply make a choice to tie up loose ends.
Finally, based on the work of Brown and Previtero
(2015) , we include a question to capture how busy re-
spondents believed themselves to be. This helps distin-
guish procrastination arising from present-biased prefer-
ences from simply missing the deadline due to other pri-
orities.
4. Background on the SURS retirement system
Our decision context is the State Universities Retire-
ment System (SURS) for the state of Illinois. 3 Employees
in the system have a one-time irrevocable choice among
three different retirement plans that have very different
features (described below). Individuals who fail to make
a choice within six months of joining the system are de-
faulted into a defined benefit (DB) plan and have no subse-
quent opportunities to alter that choice. Given that SURS-
covered employment is not covered by Social Security, the
retirement plan provided by SURS is meant to replace both
Social Security and an employer pension. As such, in ad-
dition to being very complex, this decision is enormously
consequential.
As background, SURS covers over 20 0,0 0 0 current and
former employees of over 65 Illinois universities, commu-
nity colleges, and state agencies. Participants include uni-
versity and college administrators, faculty members, cler-
ical and support staff, campus police, and others. SURS
withholds 8% of a participant’s salary as a contribution
to his/her retirement plan. Social Security taxes are not
withheld and participants do not earn credit toward So-
cial Security benefits based on their earnings from a SURS-
covered employer. The state/employer contribution for an
employee varies by retirement plan type, and because all
SURS participants are employees of the state of Illinois,
these employer contributions are a general state obligation.
From its inception in 1941 until 1997, all participants
in SURS were covered by a traditional defined benefit
plan. In 1997, the Illinois legislature passed a law allow-
ing SURS-covered employers to offer participants a choice
from among three plans, and virtually all did so by 1999.
The choice now offered by SURS employers is extremely
complex due to the myriad ways in which the three plans
differ.
The defined benefit plan, called the “Traditional Plan,”
remains one of the three plan options and is the default
option for participants who do not make an active choice
within six months of the date that SURS receives certifi-
cation of their employment. Participants contribute 8% of
salary for the Traditional Plan, an amount that is meant to
cover the employee’s share of the normal retirement ben-
efit, automatic annual increases in retirement benefits, and
3 The discussion of institutional details updates a prior discussion of
SURS in Brown and Weisbenner (2009) , where a more detailed descrip-
tion of the SURS retirement plan options can be found. We note that the
reduction in the number of employers covered by SURS in the two papers
reflects the combining of several campuses. Most of the factual informa-
tion about SURS is drawn from the SURS website ( www.surs.org , last ac-
cessed 12/11/2012).
survivor benefits. The state’s share of the normal cost of
maintaining the plan has varied over time, but the Illinois
legislature has a long history of under-funding the plan
and thus the state contributions are rarely made in full.
Benefits are paid as joint and survivor life annuities; single
participants can take one-eighth of their contributions plus
interest as a lump-sum at retirement in lieu of the sur-
vivor benefits. There are two formulas for calculating the
annuity—a standard defined benefit formula and a money
purchase calculation—and a participant receives the larger
of the two amounts (State Universities Retirement System
of Illinois, 2009) . The money purchase formula was elimi-
nated for new participants in 2005. While the Traditional
Plan is fairly generous for those who retire from the sys-
tem, it is less so for those who leave early.
The second plan option, the “Portable Plan,” is similar
to the Traditional Plan but has a few key differences. First,
if a participant leaves the SURS system before retirement
and takes a refund (i.e., “cashes” out his/her pension), s/he
receives a much higher refund than under the Traditional
Plan. Second, those who refund from the Portable Plan
receive a dollar-for-dollar matching contribution from the
employer, whereas those who refund from the Traditional
Plan receive only employee, and not employer, contribu-
tions. Third, the effective interest rate for the Portable Plan
is determined annually by the SURS Board of Trustees and
is typically higher than the rate provided by the Traditional
Plan. For example, the Traditional Plan provides an interest
rate on contributions of 4.5%, whereas the interest rate ap-
plied on Portable Plan funds has averaged 8.8% over the
period from September 1989 through June 2010. Fourth, if
a participant retires from the SURS system, the Portable
Plan benefit is paid as a single life annuity, and married
participants must accept an actuarial reduction to convert
it to a joint and survivor annuity. Thus, for participants
who leave SURS service and take refunds, the Portable Plan
is more generous than the Traditional Plan, but for those
who retire from the SURS system the benefits from the
Portable Plan are not as generous as those from the Tra-
ditional Plan.
The third plan option, the “Self-Managed Plan,” is a
participant-directed defined contribution (DC) plan that in-
vests 14.6% to 15.1% of salary (8% from the employee and
between 6.6% and 7.1% from the employer 4 ) into a partic-
ipant’s account. Participants are able to choose from a va-
riety of mutual funds and annuity contracts from Fidelity
and TIAA-CREF. Upon full vesting after five years of service,
a participant who leaves SURS service is entitled to a full
refund of both employer and employee contributions plus
investment gains/losses. Upon retirement, the participant
can choose from a wide range of annuities or a lump-sum
distribution.
Participants must make their choice of retirement plan
within six months of the date on which SURS receives cer-
tification of employment from the employer (which is es-
sentially the date of hire). If they do not do so, they are
4 The 6.6% rate was in effect from the plan’s inception until the past
few years. More recently, the rate has risen as SURS has determined that
the cost of providing disability benefits to Self-Managed Plan participants
is not as high as previously calculated.
482 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
automatically enrolled in the default option, which is the
Traditional Plan. Importantly, plan choice, including enroll-
ment in the Traditional Plan by default, is permanent and
irrevocable.
A complete comparison of the three plans is extremely
complex and involves consideration of multiple informa-
tion items, some of which are not immediately evident in
the basic enrollment materials. For example, a participant
who leaves SURS service can take a lump-sum refund, but
the difference in the refund between the Portable and Self-
Managed Plans is small prior to being vested (which is less
than five years for most participants) but is much larger af-
ter vesting. For participants who retire from SURS, the ex-
pected value of the Traditional or Portable Plans is higher
than that of the Self-Managed Plan due to factors such as
differing match rates, differing interest rate assumptions,
and more generous annuitization rates in the Traditional
and Portable Plans than are available in the private sector.
There are also countless other complexities that make it
very difficult to make an optimal plan choice.
In light of the complexity and importance of this de-
cision, it is a natural setting in which to understand
decision-making heterogeneity and its impact on default
behavior.
5. Survey design and sample statistics
5.1. Survey methods
In cooperation with administrators at SURS, we admin-
istered a web-based survey of SURS participants. The target
population was participants with an active email address
on file who joined the system in or after 1999, to ensure
that the participants made their SURS plan choice as new
employees. SURS sent these participants an email in Au-
gust 2012 inviting them to participate in the survey, with
a link to the online survey if they wished to do so. Partici-
pants received two subsequent reminder invitations in ap-
proximate two-week intervals. In total, out of 60,625 valid
emails, 6065 usable responses were received, for a 10% re-
sponse rate. This response rate compares favorably to re-
sponse rates for surveys of individual investors of 4% and
6% found in Hoffmann, Post, and Pennings (2013) and Dorn
and Sengmueller (2009) , respectively. Several other surveys
in finance report response rates around 4–9% for chief fi-
nancial officers (e.g., Graham and Harvey, 2001; Brav, Gra-
ham, Harvey, and Michaely, 2008; Dichev, Graham, Harvey,
and Rajgopal, 2013 ) and 4–6% for institutional investors
(e.g., Farnsworth and Taylor, 2006; McCahery, Sautner, and
Starks, 2015 ).
SURS sent four separate invitations, one each for ac-
tive choosers of each of the three plans and one for
those who defaulted into the Traditional Plan (all sur-
veys were approved by the Institutional Review Boards at
the authors’ institutions). Thus, the data capture respon-
dents’ actual plan choices as listed in SURS administrative
records, as well as whether the plan choice was active or
by default. The four surveys were virtually identical. They
differed because of the different text that was needed to
reveal what plan respondents were in according to admin-
istrative records (and whether they had defaulted). This
administrative plan enrollment information (e.g., “Accord-
ing to SURS administrative records, you are enrolled in the
PORTABLE PLAN”) appeared after respondents were asked
to self-report plan enrollment. This allowed us to capture
whether a respondent’s self-reported plan enrollment dif-
fered from that in the administrative records as a high-
level test of plan knowledge, and to do so in a manner
that did not require acquisition of any personally identi-
fiable information.
The survey questions capture three broad categories
of data. First, questions capture respondents’ basic demo-
graphic and economic information such as gender, mar-
ital status, age, employment, education, income, and net
worth. Several questions capture risk preferences, invest-
ment skills, and financial literacy.
Second, questions capture respondents’ experiences
with and recollections of SURS plans, the enrollment pro-
cess, and their desire to switch plans if they could. Af-
ter being asked their recollections of their plan enroll-
ment status, all respondents’ actual enrollment status per
SURS records was revealed to them; this revelation differed
across the four surveys.
Third, the survey includes four validated scales from
prior judgment and decision-making literature to capture
respondents’ decision-making approaches relevant to our
context. These scales have been extensively used in prior
research, including work on consumer behavior, the ef-
fects of decision-making on well-being, cross-cultural dif-
ferences, and choice in specific contexts (e.g., health, health
care, career, and other lifestyle choices). Given the number
of questions required to construct these scales, these ques-
tions were interspersed throughout others on the survey to
minimize respondent fatigue.
The first decision-approach scale we use is the Mel-
bourne decision-making questionnaire ( Mann, Burnett,
Radford, and Ford, 1997 ), a 22-item scale to assess an in-
dividual’s approach to decision-making in the presence of
decision conflict because of uncertainty. Four sub-scales
are constructed from the Melbourne questions.
• Procrastination (five questions): Delaying decisions,
which we hypothesize will positively predict default.
• Vigilance (six questions): A thorough analysis of alter-
natives, which is consistent with an economist’s defi-
nition of optimizing behavior; we hypothesize this will
negatively predict default.
• Hypervigilance (five questions): An anxious process of
hastily settling on an answer, which we hypothesize
will negatively predict default.
• Buck-passing (six questions): Leaving decisions to oth-
ers, which we hypothesize will positively predict de-
fault.
The decision-making approaches measured by the Mel-
bourne scale are quite relevant to our context—a com-
plex, multi-alternative, irrevocable, and time-constrained
choice before default. Indeed, as Mann, Burnett, Radford,
and Ford (1997) note, the assumption underlying the Mel-
bourne scale is one in which three conditions influence
the choice of decision approach, each of which is satis-
fied by our setting. Specifically, the authors note “Janis and
Mann’s (1977 , p. 2) conflict model is essentially a social
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 483
psychological theory of decision making in which the pres-
ence or absence of three antecedent conditions are held
to determine reliance on a particular coping pattern. The
three conditions are: (1) awareness of serious risks about
preferred alternatives, (2) hope of finding a better alterna-
tive, and (3) belief that there is adequate time to search
and deliberate before a decision is required.”
The survey also includes a 15-item scale to measure
compulsive indecisiveness ( Frost and Shows, 1993 ). Inde-
cisiveness is the tendency of an individual to avoid making
decisions, which in our setting could lead one to default.
To measure the propensity for regret, the survey in-
cludes a five-item scale from Schwartz, Ward, Monterosso,
Lyubomirsky, White, and Lehman (2002) . Our decision con-
text is one in which the alternatives are permanently for-
gone after one chooses or is defaulted and thus the poten-
tial for regret could be especially salient.
Finally, the survey includes Roets and Van Hiel’s
(2011) 15-item questionnaire to measure an individual’s
need for cognitive closure, defined as a “desire for an an-
swer on a given topic, any answer … compared to con-
fusion and ambiguity” ( Webster and Kruglanski, 1994 , p.
1049). The full Need for Closure Scale was developed by
Webster and Kruglanski (1994) , but given constraints on
survey length, the survey includes the shorter, 15-question
version from Roets and Van Hiel (2011) . We hypothesize
that people with a need for cognitive closure are more
likely to make an active decision rather than default.
While procrastination is likely important to measure
when studying default behavior, the ability to control for
the three other decision-making approaches in the Mel-
bourne Questionnaire (as well as a few others) is impor-
tant to our study. This allows us to control for some ten-
dencies that could be correlated with procrastination but
could have their own independent effects on the likelihood
of defaulting, giving us greater confidence in our ability to
isolate the effect of procrastination.
5.2. Sample and summary statistics
Table 1 shows that of our 6065 respondents, 27% de-
faulted into the Traditional Plan, 19% actively chose the
Traditional Plan, 34% chose the Portable Plan, and 20%
chose the Self-Managed Plan. 5 In our data, plan enrollment
is classified based on SURS administrative data rather than
self-reported responses of plan enrollment. It is nonethe-
less notable that respondents are knowledgeable about
their plan selection—92% of respondents correctly identi-
fied the plan in which they are actually enrolled in the
survey (with high knowledge rates regardless of plan en-
rollment). These rates of correct plan reporting are sub-
stantially higher than the 77% found in Gustman and Stein-
meier (2005, Table 2 ), suggesting that SURS participants
5 Relative to the full universe of SURS participants who have joined
the system since 1999, our sample population under-represents default-
ers and over-represents active choosers. This pattern is primarily because
those who default into the system are substantially less likely to have an
email address on file with SURS, and thus were less likely to be solicited
by the survey. Relative to the population of SURS participants who joined
the system since 1999 and who have an email address on file, our sample
proportions are much closer, as 36% of this population defaulted.
are more knowledgeable about their retirement plans than
the general U.S. population.
Table 1 also indicates that although the sample is
not nationally representative, it is nonetheless diverse in
terms of demographics, occupation, and economic back-
ground. Not surprisingly, given that this system covers
higher education, respondents are highly educated, with
62% holding a Master’s degree, professional degree, or
Ph.D. Among the remaining respondents, 10% have no post-
secondary degree, another 6% have an Associate’s degree,
and just over 20% have a Bachelor’s degree. Respondents
also come from a range of occupations, with about 13%
employed as tenured or tenure-track faculty, and 25% non-
tenure-track faculty. The remaining occupations are spread
amongst academic professionals, executives, support staff,
and maintenance and public safety personnel. There is also
substantial variation in income and household net worth.
Table 2 summarizes the distribution of responses for
the measures of decision-making approaches. Recall that
every question is asked (or reverse-coded) on a five-point
scale, with one being “strongly disagree” and five being
“strongly agree.” Each decision-approach measure is com-
puted as the average of the five-point response for each of
the questions associated with that approach, and thus can
range from 1.0 to 5.0. Interestingly, the average respondent
in our sample views themselves as being vigilant (that is,
a careful optimizer), with an average score of 4.1. The av-
erage person disagrees with the characterization of being a
procrastinator, with an average score of 1.9. Perhaps more
important for our purposes, however, is that the standard
deviation of responses is between 0.5 and 0.7, which is
useful to keep in mind when evaluating the magnitude of
the coefficients below.
6. Results
6.1. Factors associated with the likelihood of default
6.1.1. Decision-making approaches as determinants of default
Table 3 provides the result of a linear probability model
(ordinary least squares) with the dependent variable set
to one if respondents defaulted into the Traditional Plan
and zero if they made an active choice of plan (i.e., picked
any of the three plans before the six-month deadline).
Coefficients are rescaled by multiplying them by 100 so
that they represent percentage points. All specifications
are also estimated using a nonlinear probit model, and
the marginal effects are nearly identical to those reported.
Since respondents occasionally skipped or chose not to re-
spond to a question in the survey, dummy variables cap-
ture non-responses for the various explanatory variables so
an observation is not lost because of a non-response for a
particular variable. These dummy variables are included in
the regressions reported in Tables 3–5 (though the coeffi-
cients are not reported for the sake of brevity).
Two decision-making approaches are significant predic-
tors of default. Respondents who are prone to procrastinate
are significantly more likely to default (coefficient = 3.9).
While one-fifth of respondents answered “strongly dis-
agree” to all five procrastination questions (i.e., a score of
1.0 out of 5.0), one-ninth of respondents averaged at least
484 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Table 1
Summary statistics for sample, percent of respondents reported.
This table reports descriptive statistics for the sample of 6065 State Universities Retirement System of Illinois partic-
ipants who completed a survey in August 2012. These descriptive statistics are based on survey questions that capture
respondents’ basic demographic and economic information such as gender, marital status, age, employment, education,
income, and net worth, and questions that capture risk preferences, investment skills, and financial literacy.
Actual plan enrollment Demographic characteristics
Traditional (defined benefit), by default 26.9% Age (when joined SURS, in years) – mean 48
Active choice of: Age (when joined SURS, in years) – 25th% 35
Traditional Plan (defined benefit) 19.0% Age (when joined SURS, in years) – 75th% 60
Portable Plan (hybrid) 33.6% Female? 56.8%
Self-Managed Plan (defined contribution) 20.5% Married? 72.2%
Correctly identified plan in survey Have children? 67.7%
Full sample 91.9% Ranking of health relative to others
Traditional (defined benefit), by default 96.4% Very poor or poor 2.9%
Active choice of: Average 21.4%
Traditional Plan (defined benefit) 97.8% Good 46.4%
Portable Plan (hybrid) 84.6% Excellent 29.3%
Self-Managed Plan (defined contribution) 92.8% Economic characteristics
Risk preferences, investing skill Occupation
Risk-return tradeoff preference Support staff (secretary) 17.7%
Below average risk and return 15.1% Executive 1.9%
Average risk and return 65.4% Academic professional 23.3%
Above average risk and return 19.5% Faculty (tenured) 3.5%
Take gamble (50/50, 100% ↑ or 33% ↓ )? Faculty (tenure-track, not tenured) 9.5%
No 62.1% Faculty (non-tenure track) 25.0%
Yes 18.8% Police, fire, and public safety personnel 1.5%
Don’t know 19.1% Maintenance and facilities personnel 3.6%
Self-assessment of investment skill Other 14.1%
Much or slightly worse than others 31.7% SURS-covered job income
Same as others 38.5% Less than $20,0 0 0 18.3%
Slightly or much better than others 29.8% $20,0 0 0 to $39,999 23.0%
Belief of how long stay in SURS $40,0 0 0 to $59,999 25.1%
Expected to stay rest of career when joined $60,0 0 0 to $79,999 17.8%
Not at all or slightly likely 50.7% $80,0 0 0 to $99,999 6.8%
Moderately likely 12.5% $10 0,0 0 0 to $119,999 3.9%
Very or extremely likely 36.8% $120,0 0 0 or more 5.0%
Belief of political risk Share of family income in SURS-covered job
Not at all confident in Illinois legislature 71.8% 0–24% 21.5%
Slight or more confidence in Illinois 28.2% 25–49% 20.5%
General knowledge 50–74% 21.7%
Basic financial literacy – Correctly answered 75–100% 36.3%
both questions 43.2% Household net worth
Education Less than $20,0 0 0 13.7%
Less than Associate’s degree 10.2% $20,0 0 0 to $49,999 11.5%
Associate’s degree 6.2% $50,0 0 0 to $99,999 19.9%
Bachelor’s degree 21.3% $10 0,0 0 0 to $249,999 25.3%
Master’s or professional degree 42.6% $250,0 0 0 to $499,999 14.1%
Ph.D. 19.7% $50 0,0 0 0 or more 15.4%
College degree in finance or business? 18.5% For sample of defaulters
Work experience in finance? 35.8% Pick a different plan if can re-do choice today?
SURS-specific knowledge Would stay with Traditional Plan 40.2%
Basic SURS knowledge – Correctly answered Would switch to diff. plan or don’t know 59.8%
both questions 59.6% Have strong desire to switch plans 16.6%
Sample size of all respondents 6065
Sample size of defaulters 1630
a 3.0 on this scale (indicating at least some agreement
with having a tendency to procrastinate). Such a difference
in procrastination is associated with a 7.8% higher likeli-
hood of defaulting into the Traditional Plan (3.9 ×2). An-
other way to assess the economic importance of procrasti-
nation is to measure the effect of a one-standard-deviation
change in this variable on defaults. Recall that the stan-
dard deviation on this variable is 0.7 (on a five-point scale).
Thus, a one-standard-deviation increase in this measure
of procrastination is associated with a 2.7% (3.9 ×0.7) in-
crease in the likelihood of default, or a 10% increase rela-
tive to the baseline default rate of 27%.
The simplest interpretation is that those who procrasti-
nate are less likely to choose a plan, and thus are more
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 485
Table 2
Summary statistics for decision-making approaches and being in a hurry.
This table is based on a sample of 6065 State Universities Retirement System of Illinois participants
who completed a survey in August 2012. It reports summary statistics for decision-making approaches
and a measure of being busy (in a hurry). The various measures of decision-making approaches are
based upon the responses to questions for each approach, with each individual response scored from
one (strongly disagree) to five (strongly agree). The decision-making approach score is then the av-
erage of responses for the questions pertaining to that approach and thus can vary from 1.0 to 5.0.
Specifically, measures of procrastination (5-question index), vigilance (6-question index), hypervigi-
lance (5-question index), and buck-passing (6-question index) are from Mann, Burnett, Radford, and
Ford (1997) , i.e., the Melbourne Decision-Making Questionnaire. The tendency to regret (5-question
index) is from Schwartz, Ward, Monterosso, Lyubomirsky, White, and Lehman (2002) . Indecisiveness
(15-question index) is from Frost and Shows (1993) . The need for cognitive closure (15-question index)
is from Roets and Van Hiel (2011) . Finally, our measure for being busy is based on the response to a
single question “To what extent do you disagree or agree with the following statement: “I often do
not have time to fully consider all of my options because I am always in a hurry,” with the response
scored from one (strongly disagree) to five (strongly agree).
Mean S.D. 10th% 25th% 50th% 75th% 90th%
Decision-making approaches:
Procrastination 1 .9 0 .7 1 .0 1 .2 2 .0 2 .4 3 .0
Vigilance 4 .1 0 .5 3 .5 3 .8 4 .0 4 .3 4 .8
Hypervigilance 2 .4 0 .7 1 .6 2 .0 2 .4 2 .8 3 .4
Buck-passing 2 .0 0 .7 1 .0 1 .3 2 .0 2 .3 3 .0
Tendency to regret 2 .7 0 .7 1 .8 2 .2 2 .6 3 .2 3 .6
Indecisiveness 2 .4 0 .5 1 .8 2 .1 2 .4 2 .7 3 .1
Need for cognitive closure 3 .1 0 .5 2 .5 2 .9 3 .1 3 .5 3 .7
Busy/hurried:
Often do not have time to
fully consider options
because always in a hurry
2 .8 1 .1 1 .0 2 .0 3 .0 4 .0 4 .0
likely to find themselves defaulted. 6 Economists tend to
view procrastination as being a manifestation of present-
biased preferences. Present-biased individuals put too little
weight on time-distant outcomes (in this case, retirement
preparedness) relative to the near-term cost of making an
active decision. It is worth noting that there are other rea-
sons besides present bias that an individual could pro-
crastinate. For example, procrastination could simply result
from being busy or not having enough time. Thus, as a
simple control, a separate question measures the extent to
which a respondent is too busy or in too much of a hurry.
The coefficient on this “busy” measure is not significant,
nor does it alter the magnitude of the coefficient on pro-
crastination. The correlation between procrastination and
defaulting may not be causal. For example, the results pre-
sented thus far cannot rule out the possibility that procras-
tinators find the Traditional Plan to be the best choice and
deliberately default to end up in their desired plan with
the least possible effort. This concern is addressed below
by examining the extent to which procrastinators are more
or less likely to desire to stay in the same plan when asked
at a later time.
6 Respondents who tend to procrastinate may be attracted by an op-
portunity to avoid doing certain tasks. While completing our survey may
be a way to procrastinate on other tasks, we told participants upfront
that the survey would take around 20 minutes, and the survey itself had
38 screens of questions. As such, given its length, our survey is probably
not ideal for purposes of providing a simple distraction to respondents.
Even if some respondents viewed completing the survey as a means to
procrastinate on other tasks, this suggests we have a higher fraction of
procrastinators in our sample, but would not have a clear bias on the ef-
fect of procrastination on defaulting or a desire to switch plans, which is
the ultimate focus of the paper.
Consistent with our hypothesis, respondents with a
stronger need for cognitive closure are less likely to de-
fault. Individuals who have a need for closure are believed
to be uncomfortable with ambiguity and strongly desire to
arrive at “an answer” (although not necessarily the “right
answer”). The coefficient on the need for closure is −4.5.
Thus, a one-standard-deviation increase (0.5 units on the
five-point scale as reported in Table 2 ) leads to a −2.3%
reduction in the likelihood of defaulting, or more than an
8% reduction relative to baseline default rates.
Interestingly, the coefficient on vigilance—the one ap-
proach that is most closely associated with the economic
view of rational decision-making—is not significantly dif-
ferent from zero. Likewise, the other measures of decision-
making approaches are not significant predictors of default
behavior.
6.1.2. Economic and demographic determinants of default
Continuing with Table 3 , results show that risk prefer-
ences and self-assessed investment skills are significantly
correlated with default probabilities. Two questions assess
risk preferences. The first is modeled on a question used in
the Survey of Consumer Finances (SCF) that asks respon-
dents whether they would prefer to take above (below)-
average risk for above (below)-average returns. The pattern
of significant coefficients indicates that less risk-averse
respondents are less likely to default, which could re-
flect that respondents who are comfortable taking financial
risks could be more likely to prefer the defined contribu-
tion plan option, which requires an active choice. However,
a second question based upon the risk aversion questions
in the Health and Retirement Survey (HRS) asks respon-
dents to choose between their current income or a 50/50
gamble between doubling their salary or seeing it cut by
486 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Table 3
Regression of whether defaulted into retirement plan choice.
This table is based on a sample of 6065 State Universities Retirement System of Illinois participants who completed a survey in August 2012. It reports
coefficients from a linear probability model (OLS) in which the binary dependent variable is equal to one if respondents defaulted into the Traditional Plan
and zero if they made an active choice of plan. Coefficients are multiplied by 100 so they are expressed in percentage points. Standard errors, shown in
parentheses, allow for heteroskedasticity. ∗∗∗ , ∗∗ , ∗ indicates significance at the 1%, 5%, and 10% levels, respectively.
Decision-making approaches Belief of how long stay in SURS
Procrastination 3.9 ∗∗∗
(1.2)
Expected to stay rest of career when
joined
Vigilance 0.9
(1.4)
Moderately likely 0.2
(1.8)
Hypervigilance −0.9
(1.4)
Very or extremely likely 6.3 ∗∗∗
(1.4)
Buck-passing −0.5
(1.1)
Belief of political risk
Tendency to regret 0.4
(1.0)
Not at all confident in Illinois
legislature
−1.9
(1.4)
Indecisiveness −0.5
(1.8)
General knowledge
Need for cognitive closure
−4.5 ∗∗∗
(1.5)
Basic financial literacy – Correctly
answered both questions
−0.0
(1.2)
Busy/hurried Education
Often do not have time to fully
consider options because always in
a hurry
-0.6
(0.6)
Associate’s degree −4.6
(3.0)
Risk preferences, investing skill Bachelor’s degree −3.4
(2.5)
Risk-return tradeoff preference Master’s or professional degree −4.6 ∗
(2.6)
Average risk and return
−5.0 ∗∗∗
(1.8)
Ph.D.
−8.3 ∗∗∗
(2.9)
Above average risk and return
−9.5 ∗∗∗
(2.1)
College degree in finance or business? −2.0
(1.7)
Take gamble (50/50, 100% ↑ or 33% ↓ )? Work experience in finance? 1.9
(1.4)
Yes 3.3 ∗∗
(1.6)
SURS-specific knowledge
Don’t know −0.3
(1.6)
Basic SURS knowledge – Correctly
answered both questions −4.5 ∗∗∗
(1.3)
Self-assessment of investment skill Demographic characteristics
Same as others
−4.0 ∗∗∗
(1.5)
Age (when joined SURS, in years) −0.10
(0.06)
Slightly or much better than others
−5.7 ∗∗∗
(1.7)
Female? −2.8 ∗∗
(1.2)
Married? −0.6
(1.5)
Have children? 2.7 ∗∗
(1.3)
Demographic characteristics (cont.) SURS-covered job income
Ranking of health relative to others $20,0 0 0 to $39,999 −2.3
(2.6)
Average 3.5
(3.6)
$40,0 0 0 to $59,999 -2.5
(2.7)
Good −0.5
(3.5)
$60,0 0 0 to $79,999 −1.6
(2.9)
Excellent −0.9
(3.5)
$80,0 0 0 to $99,999 −3.0
(3.5)
Economic characteristics $10 0,0 0 0 to $119,999 −8.0 ∗∗
(3.7)
( continued on next page )
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 487
Table 3 ( continued )
Occupation $120,0 0 0 or more −7.3 ∗
(3.8)
Executive −0.0
(4.4)
Share of family income in
SURS-covered job
Academic professional −2.4
(2.1)
25–49% −3.3
(2.5)
Faculty (tenured) 0.5
(3.8)
50–74% −1.3
(2.6)
Faculty (tenure-track, not tenured) −5.6 ∗∗
(2.8)
75–100% −0.5
(2.6)
Faculty (non-tenure track) 5.0 ∗∗
(2.4)
Household net worth
Police, fire, and public safety
personnel
−4.1
(4.9)
$20,0 0 0 to $49,999 −0.9
(2.6)
Maintenance and facilities personnel 2.7
(3.6)
$50,0 0 0 to $99,999 −2.6
(2.3)
Other −0.9
(2.1)
$10 0,0 0 0 to $249,999 −5.3 ∗∗
(2.3)
$250,0 0 0 to $499,999
−9.1 ∗∗∗
(2.6)
$50 0,0 0 0 or more
−11.1 ∗∗∗
(2.6)
Fixed effects for year of enrollment? Yes
Fixed effects for employer? Yes
Adjusted R-squared of regression 0.100
Sample size 6065
a third. Here, those most willing to take the gamble are
more likely to default. Reconciling this offsetting pattern of
coefficients in these two questions is admittedly puzzling;
notably, though, the economic and statistical significance is
much larger for the response to the SCF question (i.e., less
risk-averse respondents are less likely to default) than for
the HRS question.
Respondents with more confidence in their own invest-
ing skills are less likely to default. This result could re-
flect a general level of comfort making financial decisions.
It could also again reflect the fact that these respondents
could be more interested in participating in the defined
contribution plan option. 7
Respondents who report that it is very or extremely
likely they will remain in SURS-covered employment for
the rest of their career are more likely to default. At first
blush, this finding seems counterintuitive because those
with longer career horizons with a SURS employer should
view pension choice as an even more consequential de-
cision. However, those with shorter horizons likely find
the Portable or Self-Managed Plans more attractive op-
tions than the Traditional Plan because of the more favor-
able cash-out terms offered by the two former plans once
7 We also estimated a regression analogous to that in Table 3 with the
choice of the Self-Managed Plan as the outcome variable. In untabulated
results, we find significantly positive (and large in magnitude) effects of
a willingness to take risk and a high self-assessment of investment skill
on selection of the Self-Managed Plan. This result suggests that the nega-
tive effects of these variables on defaulting indeed reflects these types of
respondents’ interest in the Self-Managed Plan.
SURS-covered employment ends, thus explaining the posi-
tive coefficient on expected job tenure in Table 3 .
The top of the right-hand column includes the coeffi-
cient on a question that controls for perceptions of politi-
cal risk. The poor funding status of public pensions in Illi-
nois is widely known; nearly three-quarters of respondents
stated that they were “not at all confident” in the Illinois
state legislature, and thus, one might think that greater
concern about funding would make one less likely to de-
fault into the poorly funded defined benefit plan. However,
active choice does not necessarily protect one from politi-
cal risk: only the Self-Managed Plan avoids it because both
the Traditional and Portable Plans are supported by the
same funds. Thus, while the coefficient on lacking confi-
dence in Illinois government is negative, it is insignificantly
different from zero.
Respondents with more education are less likely to de-
fault. Specifically, those with a Master’s or Professional
degree are 4.6% less likely to default, and those with a
Ph.D. are 8.3% less likely to default. Conditional on educa-
tion, measures of financial literacy 8 and degrees or work
experience in finance or business do not explain default
8 We define a respondent as having basic financial literacy if s/he an-
swered two questions correctly. One component to our financial literacy
measure is being able to identify that a savings account of $200 earning
10% per year will grow in two years to “more than $240” (as opposed to
“less than $240” or “exactly $240”). The second component is answering
that the stock of an individual company is at least as risky as a mutual
fund of U.S. stocks, which is at least as risky as a mutual fund of U.S.
government bonds, which is at least as risky as a money market fund (as
revealed by individual risk rankings of the various investments). Just over
two-fifths of the sample answered both questions correctly.
488 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
behavior. The exception is that respondents who are able
to correctly answer two basic questions about the SURS
system (the approximate share of salary employees con-
tribute to the plan, and whether they pay Social Secu-
rity tax on SURS income) are less likely to default. 9 How-
ever, we caution against drawing a causal inference be-
cause respondents who chose the Self-Managed Plan could
have learned more about these issues during the process
of making an active choice or even after the fact.
Finally, results show that women are less likely to de-
fault, whereas those with children are more likely to do
so. Results also show differences in default rates based on
occupation, income, and net worth. Specifically, higher in-
come and higher net worth respondents are significantly
less likely to default.
In sum, the data in Table 3 indicate that the probabil-
ity of default is inversely related to financial sophistica-
tion and wealth. More highly educated respondents, those
with more confidence in their investment skills, those with
more basic SURS knowledge, and those with higher income
and net worth are all significantly less likely to default.
Even after conditioning on these and other factors, how-
ever, individual decision-making approaches like procrasti-
nation matter in explaining default behavior.
6.2. An implication of defaults: the subsequent desire to
switch plans
6.2.1. Average differences by plan in desire to switch
Whether the reliance on default options enhances or
detracts from social welfare depends largely on how the
default affects the utility of individuals who accept the de-
fault relative to whatever action they would have taken
otherwise. We are not aware of any research that has at-
tempted to address this difficult question, nor do we ad-
dress it directly here because we are unable to vary the
default or measure the utility consequences of the various
plan choices.
Nonetheless, our research can inform this issue. Sur-
vey respondents were asked, “If you could go back in time
and re-do your original pension choice (assuming the rules
when you joined SURS are still in place), which plan would
you choose?” This question allows us to measure whether
the respondent views his or her plan as the best choice
as of the time of the survey, or whether s/he now prefers
a different plan. 10 This does not measure ex ante expected
utility at the time of the decision, nor is it an ex post mea-
sure of derived utility by the end of life. It does, however,
9 Specifically, we code respondents as having “basic SURS knowledge”
if they know both that they do not pay Social Security taxes on income
from their SURS employment and that they contribute in the range of
6–10% of salary as an employee contribution (the actual number is 8%).
Three-fifths of the sample answered both basic-SURS-knowledge ques-
tions correctly. 10 The reason we include the phrase “assuming the rules when you
joined SURS are still in place” is because of a reform that was imple-
mented January 1, 2011 for new hires after that date that reduced the
generosity of various retirement plans offered under SURS. The reforms
only affected new hires and thus, we wanted to be clear in our ques-
tion that respondents hired before this date expressing a desire to switch
plans would not be affected by the recent reform.
measure the preferences of respondents at the time of the
survey, and as such, it is relevant for assessing overall sat-
isfaction with the plans.
Of course, even if respondents reported that they would
choose to enroll in a different plan, the reasons behind
such responses are important. For example, if personal cir-
cumstances changed (e.g., changes in tenure, marital, or
health status), then a desire to be in a different plan does
not necessarily mean that the original choice was ex ante
suboptimal given the uncertainty at the time. In contrast,
if respondents learned something about their plans that
they should have known at the time of their decisions,
or did not select pension plans “best” for them because
they did not get around to making a decision before the
deadline, then there is concern that the default guided at
least some participants into suboptimal decisions. While
analyses of these and other factors follow in more de-
tail later, simple tabulations provide some insight into this
issue.
Fig. 1 reports the fraction of respondents who would
choose the same versus different retirement plans than
the ones in which they are enrolled if they were allowed
to switch. Across the horizontal axis, the sample is split
into defaulters and active choosers, followed by the active
choosers divided by each of the three plans (Traditional,
Portable, and Self-Managed). For each group, the figure re-
ports the fraction who would and who would not choose
the same plan (either through selecting a different plan or
choosing “don’t know”).
Strikingly, three out of five defaulters would not choose
the same plan today, which is 21% higher than that for all
active choosers. Of course, such a comparison does not al-
low us to determine whether it is the act of defaulting
or the plan itself that leads to differences in the desire
to switch plans, and absent the ability to default differ-
ent people into different plans, these reasons are difficult
to disentangle. Nevertheless, this concern can be addressed
by holding enrollment in the Traditional Plan constant, and
comparing those who defaulted into the Traditional Plan
to those who actively chose the same plan. Even with the
same plan, the difference in the desire to switch plans is
still 16%. This result is especially striking because there is
absolutely no financial benefit to actively choosing the Tra-
ditional Plan versus allowing oneself to be passively de-
faulted into it. Contributions, benefits, program rules, and
all other aspects of the plan are identical regardless of how
a participant is enrolled in the Traditional Plan. In partic-
ular, benefits accrue from the date of hire, not the date of
enrollment.
Fig. 1 is based on a broad definition of wanting to
change plans; it includes those who responded “don’t
know” when asked which plan they would choose today,
and it does not capture the intensity with which a re-
spondent wishes to switch plans. Fig. 2 refines the mea-
sure by including only respondents who stated a differ-
ent plan choice, and that their desire to switch is strong
or extremely strong. With this measure, 17% of defaulters
have a strong desire to change plans, compared to only 7%
of active choosers. When the Traditional Plan is held con-
stant by comparing those who defaulted into the Tradi-
tional Plan and those who actively chose it, results show
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 489
Percentage of respondents in this category
0
10
20
30
40
50
60
70
80
90
100
Traditional Plan, DB (by DEFAULT)
ACTIVE CHOICE of Plan Traditional Plan, DB (Active choice)
Portable Plan, hybrid (Active choice)
Self-Managed Plan, DC (Active choice)
Want different plan or don't know Would pick same plan
Fig. 1. Percent of respondents who would choose the same vs. a different retirement plan. Based on a sample of 6065 State Universities Retirement System
of Illinois participants who completed a survey in August 2012. Of these respondents, 26.9% defaulted into the Traditional DB Plan and 73.1% made an
active choice of plan (of which 19.0% chose the Traditional DB Plan, 33.6% chose the Portable Plan, and 20.5% chose the Self-Managed DC Plan). Survey
respondents are asked, “If you could go back in time and re-do your original pension choice (assuming the rules when you joined SURS are still in place),
which plan would you choose?” This figure plots the percent of respondents who would choose a different plan (or do not know which plan they would
choose) and who would pick the same plan again, categorized by those who defaulted versus made an active choice and by plan type.
Percentage of respondents in this category
0
2
4
6
8
10
12
14
16
18
Traditional Plan, DB (by DEFAULT)
ACTIVE CHOICE of Plan Traditional Plan, DB (Active choice)
Portable Plan, hybrid (Active choice)
Self-Managed Plan, DC (Active choice)
Fig. 2. Percent of respondents who would strongly desire a different retirement plan. Based on a sample of 6065 State Universities Retirement System of
Illinois participants who completed a survey in August 2012. Of these respondents, 26.9% defaulted into the Traditional DB Plan and 73.1% made an active
choice of plan (of which 19.0% chose the Traditional DB Plan, 33.6% chose the Portable Plan, and 20.5% chose the Self-Managed DC Plan). Survey respondents
are asked, “If you could go back in time and re-do your original pension choice (assuming the rules when you joined SURS are still in place), which plan
would you choose?” This figure plots the percent of respondents who would pick a different plan and who further indicate a strong or extremely strong
desire to re-do the choice, categorized by those who defaulted versus made an active choice and by plan type.
a similar pattern: 17% of defaulters versus 8% of active
choosers strongly desire to change plans.
6.2.2. Correlates of desire of defaulters to switch plans
Having established that defaulters are more likely to
want to switch plans than active choosers, the next anal-
ysis sheds light on decision-approach, economic, and de-
mographic characteristics that are associated with the de-
sire of defaulters to switch plans. The linear probability
models in T ables 4 and 5 contain the same explanatory
variables and are conducted on the sample of respondents
who defaulted into the Traditional Plan; the only differ-
ence is the dependent variable. Specifically, Table 4 uses
the broad measure of the desire to switch plans used in
490 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Table 4
Regression of whether those defaulted into Traditional Plan would not pick same plan today.
This table is based on a sample of 6065 State Universities Retirement System of Illinois participants who completed a survey in August 2012. It reports
coefficients from a linear probability model (OLS) in which the binary dependent variable is equal to one if the respondent would not pick the same
plan today (either by picking a different pension plan or answering “don’t know” to the question, would you re-do your original pension choice) and
zero otherwise. Coefficients are multiplied by 100 so they are expressed in percentage points. The sample is all respondents who were defaulted into
the Traditional Plan. Standard errors, shown in parentheses, allow for heteroskedasticity. ∗∗∗ , ∗∗ , ∗ indicates significance at the 1%, 5%, and 10% levels,
respectively.
Decision-making approaches Belief of how long stay in SURS
Procrastination 4.7 ∗ Expected to stay rest of career when joined
(2.5)
Vigilance 1.2 Moderately likely −5.9
(2.8) (4.4)
Hypervigilance 6.3 ∗∗ Very or extremely likely −13.0 ∗∗∗(3.0) (3.1)
Buck-passing −4.1 ∗ Belief of political risk
(2.5)
Tendency to regret 3.2 Not at all confident in Illinois legislature 11.5 ∗∗∗(2.1) (3.0)
Indecisiveness 2.0 General knowledge
(3.8)
Need for cognitive closure −5.6 ∗ Basic financial literacy – Correctly −0.1
(3.0) answered both questions (3.0)
Busy/hurried Education
Often do not have time to fully consider 1.2 Associate’s degree 4.3
options because always in a hurry (1.4) (6.1)
Risk preferences, investing skill Bachelor’s degree 6.7
(5.0)
Risk-return tradeoff preference Master’s or professional degree 0.3
(5.2)
Average risk and return 1.4 Ph.D. 0.6
(3.2) (6.3)
Above average risk and return 10.7 ∗∗ College degree in finance or business? −4.7
(4.7) (4.3)
Take gamble (50/50, 100% ↑ or 33% ↓ )? Work experience in finance? 1.0
(3.3)
Yes 3.2 SURS-specific knowledge
(3.5)
Don’t know 3.2 Basic SURS knowledge – Correctly -6.2 ∗∗(3.6) answered both questions (3.0)
Self-assessment of investment skill Demographic characteristics
Same as others −4.2 Age (when joined SURS, in years) −0.83 ∗∗∗(3.2) (0.13)
Slightly or much better than others −2.5 Female? 11.3 ∗∗∗(3.9) (3.0)
Married? −4.3
(3.1)
Have children? 2.8
(3.1)
Demographic characteristics (cont.) SURS-covered job income
Ranking of health relative to others $20,0 0 0 to $39,999 -3.1
(4.8)
Average −7.6 $40,0 0 0 to $59,999 −1.2
(7.2) (5.3)
Good −12.4 ∗ $60,0 0 0 to $79,999 −0.1
(7.1) (5.7)
Excellent −8.9 $80,0 0 0 to $99,999 −2.9
(7.3) (7.4)
Economic characteristics $10 0,0 0 0 to $119,999 −4.0
(9.4)
Occupation $120,0 0 0 or more −26.0 ∗∗∗(10.0)
Executive −2.3 Share of family income in SURS-covered job
(12.0)
Academic professional −6.4 25–49% 8.3
(5.2) (5.1)
Faculty (tenured) 7.3 50–74% 5.0
(9.0) (5.0)
Faculty (tenure-track, not tenured) 6.4 75–100% 3.9
(7.0) (4.9)
Faculty (non-tenure track) 3.3 Household net worth
(5.1)
Police, fire, and public safety personnel −12.5 $20,0 0 0 to $49,999 −10.8 ∗∗(9.1) (5.0)
( continued on next page )
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 491
Table 4 ( continued )
Maintenance and facilities personnel 4.2 $50,0 0 0 to $99,999 −2.7
(7.5) (4.6)
Other 4.8 $10 0,0 0 0 to $249,999 −5.2
(4.8) (4.8)
$250,0 0 0 to $499,999 −6.4
(5.7)
$50 0,0 0 0 or more −15.9 ∗∗∗(6.2)
Fixed effects for year of enrollment? Yes
Fixed effects for employer? Yes
Adjusted R-squared of regression 0.144
Sample size 1402
23% reduction relative to the baseline desire to switch.
11 The coefficients on procrastination, hypervigilance, and buck-passing
are similar across Table 4 and 5 , both in magnitude and statistical signif-
icance. One difference across Table 4 and 5 is the coefficient on a need
for cognitive closure. While a need for cognitive closure is not associated
with a strong desire of defaulters to change plans ( Table 5 ), it is nega-
tively associated with the broader measure of the desire to change plans
that includes ‘don’t know’ responses ( Table 4 ). This negative effect is con-
sistent with a respondent not wanting to revisit a prior decision.
Fig. 1 (equal to one if the respondent would choose a dif-
ferent plan or does not know which plan s/he would pick
and zero otherwise), while Table 5 uses the refined mea-
sure used in Fig. 2 (equal to one if the respondent has a
strong or extremely strong desire to choose a different plan
and zero otherwise). As before, we rescale the coefficients
by multiplying them by 100 so they represent percentage
points. Since the results are comparable across the two ta-
bles and the Table 5 analysis focuses only on respondents
who strongly desire to re-choose a specific plan, the dis-
cussion focuses on Table 5 , with any important differences
across the two analyses noted.
First, we examine the effects of decision-making ap-
proaches. Recall that in Table 3 , procrastination is cor-
related with the probability of defaulting. One limitation
to interpreting the correlation between default and pro-
crastination is that self-aware procrastinators could sim-
ply find the Traditional Plan to be a better choice, such
as if it prevents them from having to make future invest-
ment decisions on which they know they will procras-
tinate. This reasoning would suggest that procrastinators
would be more likely to be satisfied with their plan choice
and thus be less likely to wish to change plans. In con-
trast, if procrastinators defaulted into the Traditional Plan
because they never got around to making an active deci-
sion, then they would be more likely to want to change
plans. In Table 5 , procrastinators who default are signif-
icantly more likely to express a strong desire to switch
plans. A one unit increase on the five-point procrastina-
tion scale is associated with a 4.2% increase in default-
ers having a strong desire to switch plans. Given that the
standard deviation on the procrastination measure is 0.7, a
one-standard-deviation increase in procrastination is asso-
ciated with a 2.9% (4.2 ×0.7) increase in the likelihood of
strongly wanting to switch plans—a 17% increase relative
to the baseline desire to strongly change plans of 17%. An
interpretation of these findings is that procrastinators de-
faulted not because the Traditional Plan was the right one
for them, but because they never got around to making a
choice. As such, they are more likely to subsequently wish
they had made an active choice, and that choice would
have been one of the other two plans.
One might be concerned that procrastinators’ desire to
switch plans could alternatively be driven by a general ten-
dency to want to change plans regardless of the plan they
happened to join. To test this possibility, we conducted
a falsification test in which the regressions from Tables
4 and 5 were re-estimated using the sample of respon-
dents who actively chose any plan. Consistent with our
earlier interpretation, the coefficient on procrastination is
small and statistically insignificant among active choosers.
Specifically, in the regression corresponding to Table 4 , but
using the sample of active choosers of any plan, the coeffi-
cient on procrastination is 1.3 with a standard error of 1.6,
and in the regression corresponding to Table 5 , but using
the sample of active choosers of any plan, the coefficient
on procrastination is 0.3 with a standard error of 0.9. Thus,
the finding that procrastinators who default end up desir-
ing to change plans is not simply the result of procrastina-
tors being more likely to want to switch plans regardless
of the plan they happened to join.
Vigilance, which is most closely associated with an ap-
proach that economists would label as an optimizing ap-
proach, is not correlated with a desire to switch plans.
Hypervigilance, which is described in prior literature as
an impulsive or contrived decision, is positively associated
with a desire to switch plans. However, as discussed be-
low, the coefficient on hypervigilance becomes insignifi-
cant and falls in magnitude by two-thirds when the model
includes additional controls to measure changes in the cir-
cumstances of respondents since they joined SURS, so this
result is not robust.
“Buck-passing” is negatively associated with a desire
to switch plans. Buck-passing is the tendency to leave
decision-making to others, so in our setting “buck-passers”
would be content having the pension decision made for
them, and thus are unlikely to make an active decision
to change the plan in which they are already enrolled.
The significant coefficient on buck-passing is −5.6. Thus,
a one-standard-deviation increase (0.7 units on the five-
point scale as reported in Table 2 ) leads to a 3.9% reduction
in the likelihood of strongly desiring to switch plans, or a11
492 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
Table 5
Regression of whether those defaulted into Traditional Plan would strongly desire to switch pension plans today.
This table is based on a sample of 6065 State Universities Retirement System of Illinois participants who completed a survey in August 2012. It reports
coefficients from a linear probability model (OLS) in which the binary dependent variable is equal to one if the respondent had a strong or extremely
strong desire to switch to a different pension plan and zero otherwise. Coefficients are multiplied by 100 so they are expressed in percentage points.
The sample is all respondents who were defaulted into the Traditional Plan. Standard errors, shown in parentheses, allow for heteroskedasticity. ∗∗∗ , ∗∗ , ∗
indicates significance at the 1%, 5%, and 10% levels, respectively.
Decision-making approaches Belief of how long stay in SURS
Procrastination 4.2 ∗∗
(2.0)
Expected to stay rest of career when joined
Vigilance 2.1
(2.4)
Moderately likely −2.5
(3.6)
Hypervigilance 6.2 ∗∗
(2.5)
Very or extremely likely −7.7 ∗∗∗
(2.4)
Buck-passing −5.6 ∗∗∗
(1.9)
Belief of political risk
Tendency to regret −2.7
(1.7)
Not at all confident in Illinois legislature 8.0 ∗∗∗
(2.2)
Indecisiveness −3.9
(3.0)
General knowledge
Need for cognitive closure −0.3
(2.5)
Basic financial literacy – Correctly answered
both questions
−1.0
(2.3)
Busy/hurried Education
Often do not have time to fully consider
options because always in a hurry
0.4
(1.1)
Associate’s degree 0.7
(4.2)
Risk preferences, investing skill Bachelor’s degree 6.2
(3.8)
Risk-return tradeoff preference Master’s or professional degree −0.6
(3.7)
Average risk and return 3.5
(2.5)
Ph.D. 3.4
(4.6)
Above average risk and return 15.9 ∗∗∗
(3.8)
College degree in finance or business? 4.7
(3.3)
Take gamble (50/50, 100% ↑ or 33% ↓ )? Work experience in finance? −2.4
(2.3)
Yes 4.1
(2.9)
SURS-specific knowledge
Don’t know −2.7
(2.8)
Basic SURS knowledge – Correctly answered
both questions
2.6
(2.3)
Self-assessment of investment skill Demographic characteristics
Same as others 5.3 ∗∗
(2.5)
Age (when joined SURS, in years) −0.51 ∗∗∗
(0.10)
Slightly or much better than others 5.3 ∗
(3.2)
Female? 2.9
(2.3)
Married? 1.0
(2.4)
Have children? 1.7
(2.4)
Demographic characteristics (cont.) SURS-covered job income
Ranking of health relative to others $20,0 0 0 to $39,999 −1.7
(3.5)
Average 5.5
(5.2)
$40,0 0 0 to $59,999 −0.2
(3.9)
Good 2.3
(5.0)
$60,0 0 0 to $79,999 2.0
(4.6)
Excellent 7.2
(5.2)
$80,0 0 0 to $99,999 −0.7
(6.1)
Economic characteristics $10 0,0 0 0 to $119,999 1.0
(8.3)
Occupation $120,0 0 0 or more −7.9
(7.4)
Executive −3.0
(7.3)
Share of family income in SURS-covered job
Academic professional −3.0
(4.0)
25–49% 10.1 ∗∗∗
(3.8)
Faculty (tenured) 4.3
(7.2)
50–74% 9.2 ∗∗
(3.8)
Faculty (tenure-track, not tenured) −3.6
(5.4)
75–100% 11.4 ∗∗∗
(3.7)
Faculty (non-tenure track) −0.4
(3.8)
Household net worth
( continued on next page )
J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495 493
Table 5 ( continued )
Police, fire, and public safety personnel 7.2
(8.2)
$20,0 0 0 to $49,999 −1.7
(4.4)
Maintenance and facilities personnel 9.8 ∗
(5.7)
$50,0 0 0 to $99,999 −5.0
(3.9)
Other 3.5
(3.9)
$10 0,0 0 0 to $249,999 −4.4
(4.0)
$250,0 0 0 to $499,999 −4.3
(4.6)
$50 0,0 0 0 or more −8.9 ∗
(4.6)
Fixed effects for year of enrollment? Yes
Fixed effects for employer? Yes
Adjusted R-squared of regression 0.116
Sample size 1402
Results in Table 5 show that several demographic and
economic measures are associated with a strong desire of
defaulters to switch plans. The probability of wanting to
switch plans is significantly higher for those who are more
tolerant of risk, are less likely to stay in SURS for their en-
tire career, are younger, and have a higher share of family
income accounted for by their SURS employment. While
higher self-assessed investment skills are positively and
significantly associated with a stronger desire to switch
plans in Table 5 , it is an insignificant negative predictor
in Table 4 . Interestingly, although political risk did not af-
fect the likelihood of default in Table 3 , it has a strong ef-
fect on the desire to switch plans: those who are not at all
confident in the Illinois legislature are 8.0% more likely to
strongly want to switch plans.
Our survey includes 22 questions about changes that
the respondent experienced between the dates s/he joined
SURS and our survey, such as changes to marital status
and family structure, unexpected changes to income or ex-
penditures, changes to health, expectations about length of
employment, confidence in the State’s ability to honor pen-
sion commitments, market expectations, and discovery of
what retirement plan peers had selected. An extension of
the analysis in Table 5 includes all 22 of these additional
questions. Although some are significant in their own right,
the decision to include or exclude these variables has little
effect on the coefficients on the decision-making approach
variables, with the exception of the coefficient on hyper-
vigilance, which falls substantially in magnitude and is no
longer significant. 12 Thus, we are confident that the role
of decision-making approaches like procrastination in ex-
plaining the strong desire to change plans is not spuriously
picking up unobserved changes in other life circumstances
12 Specifically, with the addition of the 22 controls for change in cir-
cumstance, the coefficients on procrastination (4.3 with a standard error
of 1.7) and buck-passing ( −5.2 with a standard error of 1.7) are virtually
identical to those in Table 5 , while the coefficient on hypervigilance falls
to 2.2 (SE = 2.3). As before, the probability of strongly wanting to switch
plans is significantly higher for those who are more tolerant of risk, have
higher self-assessed investment skills, are less likely to stay in SURS for
their entire career, are not at all confident in the Illinois legislature, are
younger, and have a higher share of family income accounted for by their
SURS employment.
or expectations. Rather, these decision-making approaches
are exerting a direct influence on the desire to switch plans
that is largely orthogonal to changing circumstances. 13
One alternative explanation of our findings is that a re-
spondent might state a preference for the same plan to
appear consistent to the researcher or to herself rather
than merely reflecting the true underlying desirability of
the plan. To address this possibility, a survey question
asked respondents to rate their agreement or disagree-
ment with the statement, “I find that I often go back and
forth concerning what I want.” Respondents who disagree
or strongly disagree with the statement are classified as
“consistent,” and those who agree or strongly agree are
classified as “inconsistent.” We can then compare, as in
Figs. 1 and 2 , the fraction of defaulters who desire to
switch plans to the fraction of active choosers that de-
sire to switch plans, and how these differences vary by
whether the respondent is generally consistent or not in
decision-making.
Using the same definition of desiring to switch plans
as in Fig. 1 , results show that there are still large differ-
ences in the fraction wishing to switch plans by whether
one defaulted for each sub-group. For those who have con-
sistent decision-making, the difference is 17.6% (54.2% of
defaulters desire to switch plans versus only 36.6% of ac-
tive choosers). For those who report inconsistent decision-
making, the difference is 27.1% (69.7% of defaulters ver-
sus 42.6% of active choosers). Thus, defaulters are much
more likely to desire to switch plans than active choosers
in both the self-reported consistent and inconsistent sub-
groups. This result also holds with the “strongly desire” to
switch plans measure from Fig. 2 . Using this definition, in-
consistent defaulters are 10% more likely to have a strong
desire to change plans than inconsistent active choosers
(17.9% versus 7.9%), and there is a very similar 9.2%
13 We also estimated an extension of Table 4 that includes the ad-
ditional 22 controls to capture changing circumstances since the re-
spondent joined SURS. The coefficients on procrastination (4.6), buck-
passing ( −3.7), and a need for cognitive closure ( −5.2) change little from
Table 4 (although the coefficient on buck-passing switches from
marginally significant to marginally insignificant). The coefficient on hy-
pervigilance falls in magnitude and is no longer significant (3.2 with a
standard error of 3.0).
494 J.R. Brown et al. / Journal of Financial Economics 121 (2016) 477–495
difference among consistent defaulters and choosers (16.6%
versus 7.4%).
7. Discussion and conclusion
This study shows significant heterogeneity across the
population with regard to sensitivity to defaults. In ad-
dition to being influenced by economic and demographic
factors such as income, wealth, knowledge, and invest-
ment skills, results show heterogeneity in the likelihood
of defaulting based on measures of different decision ap-
proaches. There is an especially robust relation between re-
spondents’ tendency toward procrastination and the likeli-
hood of default, as well as procrastinators’ subsequent de-
sire to be in different retirement plans than the one into
which they were defaulted. There are also significant ef-
fects of other decision-making approaches. For example,
those with a strong need for cognitive closure are less
likely to default, while buck-passing is negatively associ-
ated with the desire of defaulters to switch plans.
The finding that decision-making approaches such as
procrastination are associated with defaulting and the later
desire to switch plans is important for assessing the wel-
fare consequences of defaults. If individuals are subse-
quently dissatisfied with the default option, especially in
settings in which the default is irreversible, our find-
ings suggest another reason to be cautious about relying
heavily on default options relative to alternative choice
architectures. Future research could be helpful in explor-
ing these issues in additional settings and in understand-
ing what types of institutional or procedural mechanisms
might help to mitigate procrastination.
Disclosure
Brown is a Trustee for TIAA and has also received com-
pensation as a speaker, author, or consultant from a num-
ber of financial institutions that are involved in retirement
plan design. Weisbenner has received compensation as an
author of a white paper on defined-contribution plans.
Acknowledgments
This research was supported by the U.S. Social Security
Administration (SSA) through grants RRC0809840 0-04-0 0
and RRC0809840 0-03-0 0 to the National Bureau of Eco-
nomic Research (NBER) as part of the SSA Retirement Re-
search Consortium. The findings and conclusions expressed
are solely those of the authors and do not represent the
views of SSA, any agency of the Federal Government, or the
NBER. This research would not have been possible with-
out the expert and enthusiastic assistance of the State Uni-
versities Retirement System staff, especially Bill Mabe, Pam
Butler, Tracy Childress, and Byron Campbell. We thank Su-
san Anderson for cheerful and expert assistance in pro-
gramming the survey instrument and Lisa Marinelli for her
exceptional assistance guiding this project through the In-
stitutional Review Board process. We thank workshop par-
ticipants at the SSA and the University of Colorado at Boul-
der for feedback on earlier versions of this paper and an
anonymous referee for useful comments.
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